CN112053756B - Clinical specimen inspection data-based inspection result quality evaluation method and system - Google Patents
Clinical specimen inspection data-based inspection result quality evaluation method and system Download PDFInfo
- Publication number
- CN112053756B CN112053756B CN202010871944.1A CN202010871944A CN112053756B CN 112053756 B CN112053756 B CN 112053756B CN 202010871944 A CN202010871944 A CN 202010871944A CN 112053756 B CN112053756 B CN 112053756B
- Authority
- CN
- China
- Prior art keywords
- data
- evaluation
- instrument
- value
- item
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Primary Health Care (AREA)
- General Physics & Mathematics (AREA)
- Epidemiology (AREA)
- Game Theory and Decision Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- General Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a test result quality evaluation method and a test result quality evaluation system based on clinical specimen test data. The quality evaluation in hospitals and among hospitals is realized through PQA plug-ins, cloud servers, applets and related web pages thereof; the PQA plug-in automatically grabs all detection result data of each instrument inspection item in the laboratory internal information system every day, extracts characteristic values, generates a two-dimensional code and reports the two-dimensional code through a small program code scanning or generates a data packet and reports the data packet through a network; the reported data are stored in a cloud server; the cloud server carries out comprehensive comparison and evaluation on all data reported by each hospital; and outputting and displaying the evaluation result through the applet and the related webpage thereof. According to the quality evaluation method disclosed by the invention, a large amount of patient sample detection data obtained by daily detection in a hospital is utilized, the quality evaluation in the hospital and among hospitals is carried out by extracting the characteristic value of each batch of data, the quality of each batch of patient results is directly reflected, the evaluation frequency is improved, the long-term level of the hospital can be reflected, and the authenticity of the data is ensured.
Description
Technical Field
The invention relates to the technical field of medical institution patient data quality evaluation, in particular to a method and a system for evaluating quality of a test result based on clinical specimen test data.
Background
The quality evaluation of the inspection results among hospitals (the quality evaluation among rooms, EQA) is to organize a plurality of hospitals at regular intervals in the medical and health system every year to detect the same quality control sample at the same time, evaluate the difference of the inspection results of regional hospitals by feeding back detection data, and evaluate the deviation of the output results of each hospital. The EQA score is an important review index in the current hospital grade review and is also a main basis for public hospital performance assessment.
The current EQA is to calculate the relative deviation between the result of each hospital and the target value by taking the median or the average value as the target value of the detection result of the same quality control sample fed back by the hospital, and evaluate the relative deviation as "on target" when the relative deviation is within the allowable range, and evaluate the relative deviation as "off target" when the relative deviation exceeds the allowable range. In 10 different quality control sample annual detection of the present hospital, if more than 80% quality control sample results are rated as 'on target', the annual room interstitium evaluation of the hospital is qualified; otherwise, the test result is unqualified. In the prior art, the EQA has some drawbacks: 1. the frequency of the EQA development is low every year, and the quality of hospitals cannot be represented by a plurality of quality control sample results every year; 2. the EQA quality control sample has higher preparation cost, positive clinical samples can not meet the evaluation requirement of large-scale hospitals, and part of remote hospitals have high cold chain transportation cost, most hospitals can not participate in evaluating 3, the EQA quality control sample is different from a clinical sample, foreign additive substances exist, the detection process is separated from the clinical sample, and the performance of the clinical sample can not be reflected instead; 4. the quality control sample detection result is subjected to more human intervention factors, and the data authenticity is difficult to guarantee; 5. the prior proposal can only reflect the performance of the hospital instrument, but can not reflect the quality performance of the whole process from sample collection to result acquisition; 6. the current scheme can only give a conclusion that the quality control product is detected to be 'qualified' or 'unqualified' in each activity of a hospital, which is equivalent to temporarily making a 'product' to carry out quality inspection, and the conclusion is not directly related to the qualification of daily products.
Disclosure of Invention
The invention aims to provide a quality evaluation method of a test result based on clinical specimen test data, which utilizes a large amount of patient specimen test data obtained by daily detection of a hospital, performs quality evaluation in the hospital and among hospitals by extracting characteristic values of each batch of data, directly reflects the quality of each batch of patient results, improves the evaluation frequency, increases the evaluation density, can reflect the long-term level of the hospital and ensures the authenticity of the data. Correspondingly, the invention also provides a test result quality evaluation system based on the clinical specimen test data.
For the method, the technical scheme of the invention is as follows: the method comprises the following steps of S1, automatically grabbing all detection result data of each instrument inspection item in an internal information system of a laboratory every day by using a computer provided with a PQA plug-in for grabbing data, wherein the detection result data comprises instrument numbers, detection results, time and sample sources, and the sample sources refer to large classifications of patients from hospitalization, outpatient service and physical examination; s2, adopting a Box-Plot scheme to perform exception handling and outlier truncation elimination on detection results with the same specimen sources, and automatically eliminating patient results with abnormal clinical indexes; according to different projects, taking the median, mean or histogram peak value of the residual data as the characteristic value of the current day and storing the median, mean or histogram peak value into a CSV file of the corresponding project of the instrument, wherein the content comprises a detection date, an outpatient characteristic value, an inpatient characteristic value and a physical examination characteristic value; s3, generating a two-dimensional code according to the characteristic value of the instrument or the item, directly scanning the code and reporting the code through a small program, or generating a data packet according to the characteristic value of the instrument or the item, and directly reporting the data packet through a network; storing the reported data in a cloud server; s4, the cloud server performs comprehensive comparison and evaluation on all data reported by each hospital, wherein the comprehensive comparison and evaluation comprises PBRTQC evaluation, PEQA evaluation and PQA mutual recognition automatic scoring; s5, outputting and displaying comprehensive comparison and evaluation results through the applet and related webpages thereof.
Compared with the prior art, the quality evaluation method of the test result based on the clinical specimen test data has the following advantages:
(1) Through the extraction of the characteristic values of the daily patient data, the EQA can be developed, the evaluation frequency is greatly improved, the evaluation density is increased, and the long-term level of a hospital can be reflected; patient data are generated by hospital detection and cannot be changed due to human intervention, so that the authenticity of the data can be completely ensured;
(2) The characteristic value is extracted from the patient sample result, and directly represents each performance of each batch of patient result, and the evaluation of the characteristic value is the direct evaluation of the whole batch of patient results in the hospital; the dependence on quality control samples is eliminated, quality control cost is not needed, cold chain logistics is not needed, and networking rapid evaluation of thousands of hospitals is rapidly realized by using patient data generated by the hospitals per day;
(3) The evaluation of the characteristic value of the patient data covers the influence factors of the whole process from sample acquisition to result output, and is an expansion of the current EQA scheme; the existing 'product' is directly sampled for quality inspection, and the conclusion is directly related to the qualification of the daily 'product';
(4) The characteristic values can be used for accumulating and establishing basic values of all items of people around the hospital, and the health level of people in different areas can be monitored through network by evaluating the basic values.
As an optimization, in the step S1, no clinical information of the patient is acquired during data capture. Thus, concerns about patient privacy can be avoided.
As an optimization, the step S4 includes the following calculation process: the cloud server divides the instruments into a whole group and a similar group according to the difference of the items in different hospitals and instrument brands, and the items and the characteristic values corresponding to the instruments are divided into corresponding groups; ii the cloud server counts the characteristic values of each group of each project every day as a group mean value, a group standard deviation and a group variation coefficient; after the number of hospitals is increased and detection data are accumulated, obtaining a stable crowd detection basic value, fixing a later-stage stable basic value as a target value; counting the average value, standard deviation and variation coefficient of all characteristic values in each item group as a month group average value, a month group standard deviation and a month group variation coefficient in each month, and storing the index statistic values in a CSV file of a target value; iii, comparing the characteristic values of each item instrument from different sample sources with the average value of the characteristic values of the same source before each item instrument, calculating Z scores, and judging that the Z scores of the characteristic values of the two sample sources exceed 2 or-2 at the same time, and then judging that the indoor quality control of the patient data of the current day triggers an alarm; the duty ratio of days without alarm is counted in a cumulative way every month, and the duty ratio is the quality control rate PICR of the mutual identification index for evaluating the stability; iv comparing the characteristic value of each instrument item with the target value grouped on the corresponding day every day to obtain two mutual identification indexes of each day, and judging whether the real-time relative deviation pBias and the real-time standard deviation index pSDI meet the requirements; each month, counting the qualification rate of pBias and pSDI of each instrument item in whole month to obtain two mutual identification indexes of each month, wherein the qualification rate of pBias R of relative deviation and the qualification rate of pSDIR of standard deviation index are obtained; v, calculating a coefficient of variation PCV by counting the characteristic value of each instrument item for whole month, and comparing the coefficient of variation PCV with the month group coefficient of variation of the group in which the coefficient of variation is positioned to obtain an evaluation result imprecision mutual identification index and a interval coefficient of variation index PCVI; counting the difference value between the characteristic value mean value of each instrument item in whole month and the month group mean value of the group in which the instrument item is positioned, and respectively comparing the difference value with the month group mean value of the group in which the instrument item is positioned and the month group standard deviation of the instrument item to obtain an evaluation result deviation mutual recognition index, an interval relative deviation PBias and an interval standard deviation index PSDI; in the mutual recognition indexes, the evaluation of the PCV and the quality control rate PICR is PBRTQC evaluation; the evaluation of the interval variation coefficient index PCVI, the interval standard deviation index PSDI, the real-time standard deviation index pSDI, the real-time relative deviation pBias and the interval relative deviation PBias is PEQA evaluation; and calculating scores of all mutual recognition indexes in the PBRTQC evaluation and the PEQA evaluation, namely, the PQA automatic mutual recognition scores. Through the steps, the mutual recognition indexes are respectively evaluated, so that the problem of a certain aspect of the product can be accurately reflected, and the correction of the part with the problem in the implementation of the hospital is facilitated.
In the step S5, as an optimization, various comparison data and evaluation results can be checked through the applet and related web pages thereof on the platform organization manager interface, the various laboratory management mobile terminals or the various laboratory management computer terminals. Therefore, the platform organization manager can adjust and set various statistical parameters, mutual recognition index scoring weights, grouping, quality control rules and the like according to various comparison data and evaluation results; the laboratory can know the difference between the same instrument item and other hospital results through mutual recognition indexes and mutual recognition scores, so that the correction of the problematic part is realized.
For the system, the technical scheme of the invention is as follows: the quality evaluation system of the test result based on the clinical specimen test data comprises a PQA plug-in, a cloud server, an applet and a related webpage thereof; the PQA plug-in is used for automatically capturing all detection result data of each instrument inspection item in an internal information system of a laboratory, adopting a Box-Plot scheme, cutting off and rejecting abnormal values and outliers of detection results with the same sample source, automatically eliminating patient results with abnormal clinical indexes, taking the median, mean value or histogram peak value of the residual data as the characteristic value of the current day according to different items, and generating a two-dimensional code or data packet; the cloud server is used for storing the instrument and project characteristic values of each hospital reported through the applet or the network, and carrying out data comprehensive comparison and evaluation; the applet and its associated web page are used to perform user management and presentation of the rating data.
Compared with the prior art, the quality evaluation system of the test result based on the clinical specimen test data realizes the development of the EQA through the PQA plug-in, the cloud server, the applet and the related web pages thereof; the quality evaluation in hospitals and among hospitals is carried out by extracting the characteristic values of a large number of patient sample detection data obtained by daily detection in hospitals, the quality of each batch of patient results is directly reflected, the accuracy of the data is ensured, and the evaluation frequency is high.
As optimization, the applet and the related web page thereof can be used for external display, displaying the mutual recognition scores of the detection items of all hospitals, and can be accessed to an instrument reagent purchasing platform, a public health and hospital quality management system.
Drawings
FIG. 1 is a flow chart of steps of a test result quality assessment method based on clinical specimen test data of the present invention;
fig. 2 is a path and score standard diagram of the internal score calculation when the cloud server of the present invention performs the evaluation.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not intended to be limiting.
Referring to fig. 1, the test result quality evaluation method based on clinical specimen test data of the present invention includes the steps of S1, automatically grasping all test result data of each instrument test item in an information system inside a laboratory every day, including instrument number, test result, time and specimen source, by using a computer equipped with a PQA plug-in for grasping data, the specimen source referring to a large classification of patients from hospitalization, outpatient service, physical examination; s2, adopting a Box-Plot scheme to perform exception handling and outlier truncation elimination on detection results with the same specimen sources, and automatically eliminating patient results with abnormal clinical indexes; according to different items, taking the median, mean or histogram peak value of the residual data (each item is different when the characteristic value is extracted), wherein different items can use different methods, and three indexes of the median, the mean and the histogram peak value can be selected) as the characteristic value of the current day and storing the characteristic value into a CSV file of the corresponding item of the instrument, wherein the content comprises a detection date, an outpatient characteristic value, a hospitalization characteristic value and a physical examination characteristic value; s3, generating a two-dimensional code according to instruments or items, directly reporting the characteristic value through a small program code (directly reporting data through the small program code, and avoiding affecting the safety and human intervention of an internal information system of a laboratory), or generating a data packet according to instruments or items, and directly reporting the data through a network; storing the reported data in a cloud server; s4, the cloud server performs comprehensive comparison and evaluation on all data reported by each hospital, wherein the comprehensive comparison and evaluation comprises PBRTQC evaluation (self internal evaluation), PEQA evaluation (inter-room external evaluation) and PQA mutual recognition automatic scoring; s5, outputting and displaying comprehensive comparison and evaluation results through the applet and related webpages thereof.
In the step S1, no clinical information of the patient is acquired during data capture. Thus, concerns about patient privacy can be avoided.
The step S4 includes the following calculation process: the cloud server divides the instruments into an integral group and a homogeneous group according to the difference of the instruments brands of different hospitals and instruments (for a certain item, because the detected equipment is from different manufacturers, because the internationally unified standard is not available, even the measurement units are completely different, the results of the detection equipment of different manufacturers can not be directly compared, only the results among the same manufacturers can be compared with each other, which is the concept of the homogeneous group, if a certain item has the internationally standardized standard, and the measurement units can be unified and can be directly compared, the concept of the integral group is the concept of the integral group; ii the cloud server counts the characteristic values of each group of each project every day as a group mean value, a group standard deviation and a group variation coefficient; the statistical index data are established every day in the early stage, and the statistical index data are possibly slightly different every day, but the later-stage stable basic value is taken as a target value to be fixed after the stable crowd detection basic value is obtained along with the increase of the number of hospitals and the accumulation of detection data; counting the average value, standard deviation and variation coefficient of all characteristic values in each item group as a month group average value, a month group standard deviation and a month group variation coefficient in each month, and storing the index statistic values in a CSV file of a target value; iii, comparing the characteristic values of each item instrument from different sample sources with the average value of the characteristic values of the same source before each item instrument, calculating Z scores, and judging that the Z scores of the characteristic values of the two sample sources exceed 2 or-2 at the same time, and then judging that the indoor quality control of the patient data of the current day triggers an alarm; the duty ratio of days without alarm is counted in a cumulative way every month, and the duty ratio is the quality control rate PICR of the mutual identification index for evaluating the stability; iv comparing the characteristic value of each instrument item with the target value grouped on the corresponding day every day to obtain two mutual identification indexes of each day, and judging whether the real-time relative deviation pBias and the real-time standard deviation index pSDI meet the requirements; each month, counting the qualification rate of pBias and pSDI of each instrument item in whole month to obtain two mutual identification indexes of each month, wherein the qualification rate of pBias R of relative deviation and the qualification rate of pSDIR of standard deviation index are obtained; v, calculating a coefficient of variation PCV by counting the characteristic value of each instrument item for whole month, and comparing the coefficient of variation PCV with the month group coefficient of variation of the group in which the coefficient of variation is positioned to obtain an evaluation result imprecision mutual identification index and a interval coefficient of variation index PCVI; counting the difference value between the characteristic value mean value of each instrument item in whole month and the month group mean value of the group in which the instrument item is positioned, and respectively comparing the characteristic value mean value with the month group mean value of the group in which the instrument item is positioned and the month group standard deviation of the instrument item to obtain an evaluation result deviation mutual recognition index, and obtaining interval relative deviation PBias and interval standard deviation index PSDI; in the mutual recognition indexes, the evaluation of the PCV and the quality control rate PICR is PBRTQC evaluation; the evaluation of the interval variation coefficient index PCVI, the interval standard deviation index PSDI, the real-time standard deviation index pSDI, the real-time relative deviation pBias and the interval relative deviation PBias is PEQA evaluation; and the scores of the mutual recognition indexes in the PBRTQC evaluation and the PEQA evaluation are the PQA automatic mutual recognition scores. The evaluation criteria are shown in fig. 2, each unit is scored according to the arrow route sequence in fig. 2, and finally the score instruments are summarized. Referring to FIG. 2, it is first determined whether the PCV coefficient is less than 1.5, and if not less than 1.5, a reselection of the quality control material detection scheme is recommended; if the quality control rate is smaller than 1.5, the subsequent grading is started, firstly, the quality control rate PICR is graded (namely, the indoor quality control is in a control day proportion and is fully divided into 15 minutes in an evaluation time period), the interval variation index PCVI is graded (namely, the total inaccuracy index and the full divided into 15 minutes in the evaluation time period), the interval standard deviation index PSDI is graded (namely, the total standard deviation index and the full divided into 20 minutes in the evaluation time period), the real-time standard deviation index pSDI is graded (namely, the daily standard deviation index qualified day proportion and the full divided into 15 minutes in the evaluation time period), the real-time relative deviation pBias is graded (namely, the daily structure relative deviation qualified day proportion and the full divided into 15 minutes in the evaluation time period), and finally the interval relative deviation PBias is graded (namely, the total mean value relative deviation and the full divided into 20 minutes in the evaluation time period); and (5) sequentially scoring the six mutual recognition indexes, carrying out 100-point total score, and finally summarizing the scores. (of the six mutual identification indexes, the evaluation of the quality control in-control rate PICR and the interval variation coefficient index PCVI is the result stability evaluation, the evaluation of the quality control in-control rate PICR, the real-time standard deviation index pSDI and the real-time relative deviation pBias is the daily real-time evaluation, the evaluation of the interval variation coefficient index PCVI, the interval standard deviation index PSDI and the interval relative deviation PBias is the interval overall evaluation, and the evaluation of the interval standard deviation index PSDI, the real-time standard deviation index pSDI, the real-time relative deviation pBias and the interval relative deviation PBias is the result deviation evaluation), the specific scores are respectively evaluated and given through the steps, the problem on one aspect of a product can be accurately reflected, and the hospital implementation is convenient.
In step S5, each item of comparison data and evaluation results can be checked through the applet and its associated web page on the platform organization manager interface (the platform organization manager interface can check the mutual recognition index and the mutual recognition score of each item of equipment, the mutual recognition range of the patient data of the item of equipment in the monitoring area, and the abnormal change of the basic values of the crowd), each laboratory management mobile terminal or each laboratory management computer terminal (each laboratory management mobile terminal or each laboratory management computer terminal can check the stability accuracy of the item of equipment daily and monthly and know the running state of each item of equipment in real time). Therefore, the platform organization manager can adjust and set various statistical parameters, mutual recognition index scoring weights, grouping, quality control rules and the like according to various comparison data and evaluation results; the laboratory can know the difference between the same instrument item and other hospital results through mutual recognition indexes and mutual recognition scores, so that the correction of the problematic part is realized.
The invention relates to a clinical specimen inspection data-based inspection result quality evaluation system, which comprises a PQA plug-in, a cloud server, an applet and related webpages thereof; the PQA plug-in is used for automatically capturing all detection result data of each instrument inspection item in an internal information system of a laboratory, adopting a Box-Plot scheme, cutting off and rejecting abnormal values and outliers of detection results with the same sample source, automatically removing abnormal clinical index patient results, taking the median, the mean value or the histogram peak value of the residual data as the characteristic value of the current day according to different items, and generating a two-dimensional code or a data packet (when the two-dimensional code is generated, reporting the data through a small program code scanning, and when the data packet is generated, reporting the data through a network); the cloud server is used for storing the instrument and project characteristic values of each hospital reported through the applet or the network, and carrying out data comprehensive comparison and evaluation; the applet and its associated web page are used to perform user management and presentation of the rating data.
The applet and the related web page thereof can be used for external display, displaying the mutual recognition scores of the detection items of all hospitals, and can be accessed to an instrument reagent purchasing platform, a public health and hospital quality management system. The evaluation result is inserted into the inspection result mutual recognition system and the room interstitial evaluation system through the external display authority, and can be provided for inspection related departments such as reagent instrument purchase and the like for reference.
The above general description of the invention and the description of specific embodiments thereof referred to in this application should not be construed as limiting the scope of the invention. Those skilled in the art can add, subtract or combine the features disclosed in the foregoing general description and/or the detailed description (including examples) to form other technical solutions within the scope of the present application without departing from the disclosure of the present application.
Claims (5)
1. The quality evaluation method of the test result based on the clinical specimen test data is characterized by comprising the following steps of: the method comprises the following steps;
s1, automatically grabbing all detection result data of each instrument inspection item in an internal information system of a laboratory every day by using a computer provided with a PQA plug-in for grabbing data, wherein the detection result data comprises instrument numbers, detection results, time and specimen sources, and the specimen sources refer to large classifications of patients from hospitalization, outpatient service and physical examination;
s2, adopting a Box-Plot scheme to perform exception handling and outlier truncation elimination on detection results with the same specimen sources, and automatically eliminating patient results with abnormal clinical indexes; according to different projects, taking the median, mean or histogram peak value of the residual data as the characteristic value of the current day and storing the median, mean or histogram peak value into a CSV file of the corresponding project of the instrument, wherein the content comprises a detection date, an outpatient characteristic value, an inpatient characteristic value and a physical examination characteristic value;
s3, generating a two-dimensional code according to the instrument or the item, directly reporting the two-dimensional code through a small program code scanning, or generating a data packet according to the instrument or the item, and directly reporting the two-dimensional code through a network; storing the reported data in a cloud server;
s4, the cloud server performs comprehensive comparison and evaluation on all data reported by each hospital, wherein the comprehensive comparison and evaluation comprises PBRTQC evaluation, PEQA evaluation and PQA mutual recognition automatic scoring, and the calculation process is as follows:
the cloud server divides the instruments into a whole group and a similar group according to the difference of the items in different hospitals and instrument brands, and the items and the characteristic values corresponding to the instruments are divided into corresponding groups;
the cloud server counts the characteristic values of each group of each project every day, namely a group mean value, a group standard deviation and a group variation coefficient, and after the number of hospitals is increased and detection data is accumulated, a stable crowd detection basic value is obtained, and then the later-stage stable basic value is used as a target value to be fixed; counting the average value, standard deviation and variation coefficient of all characteristic values in each item group as a month group average value, a month group standard deviation and a month group variation coefficient in each month, and storing the index statistic values in a CSV file of a target value;
iii, comparing the characteristic values of each item instrument from different sample sources with the average value of the characteristic values of the same source before each item instrument, calculating Z scores, and judging that the Z scores of the characteristic values of the two sample sources exceed 2 or-2 at the same time, and then judging that the indoor quality control of the patient data of the current day triggers an alarm; the duty ratio of days without alarm is counted in a cumulative way every month, and the duty ratio is the quality control rate PICR of the mutual identification index for evaluating the stability;
iv comparing the characteristic value of each instrument item with the target value grouped on the corresponding day every day to obtain two mutual identification indexes of each day, and judging whether the real-time relative deviation pBias and the real-time standard deviation index pSDI meet the requirements; each month, counting the qualification rate of pBias and pSDI of each instrument item in whole month to obtain two mutual identification indexes of each month, wherein the qualification rate of pBias R of relative deviation and the qualification rate of pSDIR of standard deviation index are obtained;
v, calculating a coefficient of variation PCV by counting the characteristic value of each instrument item for whole month, and comparing the coefficient of variation PCV with the month group coefficient of variation of the group in which the coefficient of variation is positioned to obtain an evaluation result imprecision mutual identification index and a interval coefficient of variation index PCVI; counting the difference value between the characteristic value mean value of each instrument item in whole month and the month group mean value of the group in which the instrument item is positioned, and respectively comparing the characteristic value mean value with the month group mean value of the group in which the instrument item is positioned and the month group standard deviation of the instrument item to obtain an evaluation result deviation mutual recognition index, and obtaining interval relative deviation PBias and interval standard deviation index PSDI;
in the mutual recognition indexes, the evaluation of the PCV and the quality control rate PICR is PBRTQC evaluation; the evaluation of the interval variation coefficient index PCVI, the interval standard deviation index PSDI, the real-time standard deviation index pSDI, the real-time relative deviation pBias and the interval relative deviation PBias is PEQA evaluation; the calculated scores of each mutual recognition index in the PBRTQC evaluation and the PEQA evaluation are PQA automatic mutual recognition scores;
s5, outputting and displaying comprehensive comparison and evaluation results through the applet and related webpages thereof.
2. The method for evaluating the quality of a test result based on clinical specimen test data according to claim 1, wherein: in the step S1, no clinical information of the patient is acquired during data capture.
3. The method for evaluating the quality of a test result based on clinical specimen test data according to claim 1, wherein: in the step S5, each item of comparison data and the evaluation result are checked at the platform organization manager interface, each laboratory management mobile terminal or each laboratory management computer terminal through the applet and the related web page thereof.
4. A test result quality evaluation system based on clinical specimen test data for performing the evaluation method according to steps S1 to S5 of claim 1, characterized in that: the method comprises PQA plug-ins, cloud servers, applets and related webpages thereof; the PQA plug-in is used for automatically grabbing all detection result data of each instrument inspection item in an internal information system of a laboratory, adopting a Box-Plot scheme, cutting off and rejecting abnormal values and outliers of detection results with the same specimen source, automatically eliminating the results of patients with abnormal clinical indexes, taking the median, the mean value or the histogram peak value of the residual data as the characteristic value of the current day according to different items, and generating a two-dimensional code or a data packet, wherein the content comprises the detection date, the clinic characteristic value, the hospitalization characteristic value and the physical examination characteristic value; the cloud server is used for storing the instrument and project characteristic values of each hospital reported through the applet or the network, and carrying out data comprehensive comparison and evaluation; the applet and its associated web page are used to perform user management and presentation of the rating data.
5. The clinical specimen testing data based testing result quality evaluation system of claim 4, wherein: the applet and its associated web page are used for external display, displaying mutual recognition scores of test items of various hospitals, and/or accessing an instrument reagent purchasing platform, public health and hospital quality management system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010871944.1A CN112053756B (en) | 2020-08-26 | 2020-08-26 | Clinical specimen inspection data-based inspection result quality evaluation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010871944.1A CN112053756B (en) | 2020-08-26 | 2020-08-26 | Clinical specimen inspection data-based inspection result quality evaluation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112053756A CN112053756A (en) | 2020-12-08 |
| CN112053756B true CN112053756B (en) | 2023-08-08 |
Family
ID=73599339
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010871944.1A Active CN112053756B (en) | 2020-08-26 | 2020-08-26 | Clinical specimen inspection data-based inspection result quality evaluation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112053756B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112924661B (en) * | 2021-02-20 | 2023-09-01 | 北京中医药大学东方医院 | Data detection method, system, electronic equipment and storage medium |
| CN113469309B (en) * | 2021-05-25 | 2024-05-10 | 上海金标生物科技有限公司 | Health information processing method, device, system, equipment and storage medium |
| CN113219182B (en) * | 2021-06-03 | 2022-10-14 | 首都医科大学附属北京朝阳医院 | Method for analyzing TSH multi-system comparability by using quality evaluation data in rooms |
| CN114209345B (en) * | 2021-11-22 | 2024-05-24 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Nuclear medicine equipment and correction source quality control method, system, equipment and storage medium thereof |
| CN115116577B (en) * | 2022-07-26 | 2022-11-29 | 青岛美迪康数字工程有限公司 | Quality control data transmission method and device based on two-dimensional code |
| CN116501921B (en) * | 2023-06-25 | 2023-09-15 | 长沙计支宝信息科技有限公司 | Test data processing method and system for press machine for testing performance of building material |
| CN117275644B (en) * | 2023-08-31 | 2024-04-16 | 广州零端科技有限公司 | Detection result mutual recognition method, system and storage medium based on deep learning |
| CN117314205B (en) * | 2023-11-29 | 2024-01-26 | 吉林金域医学检验所有限公司 | Method and device for reporting detection results of compartment interstitial evaluation items |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007279059A (en) * | 2007-06-18 | 2007-10-25 | Sysmex Corp | Quality management system for clinical examination |
| JP2014182472A (en) * | 2013-03-18 | 2014-09-29 | Mizuho Information & Research Institute Inc | Health care support system, health care support method and health care support program |
| CN104424385A (en) * | 2013-08-22 | 2015-03-18 | 上海联影医疗科技有限公司 | Method and device for evaluating medical images |
| WO2016130879A1 (en) * | 2015-02-13 | 2016-08-18 | Prairie Ventures, Llc | System and method to objectively measure quality assurance in anatomic pathology |
| CN105893725A (en) * | 2014-11-13 | 2016-08-24 | 北京众智汇医科技有限公司 | Management system for an entire process of hospital infection prevention and control, and method thereof |
| CN105936904A (en) * | 2015-06-04 | 2016-09-14 | 佛山市第人民医院 | LncRNA molecule ENST00000438553 and application thereof in auxiliary diagnosis of nasopharyngeal carcinoma |
| CN107545361A (en) * | 2017-08-03 | 2018-01-05 | 广西金域医学检验所有限公司 | Compare System and method between room |
| CN108898328A (en) * | 2018-09-29 | 2018-11-27 | 柳州市妇幼保健院 | A kind of system of the hospital convenient for External quality evaluation |
| CN110423816A (en) * | 2018-10-12 | 2019-11-08 | 天津医科大学肿瘤医院 | Prognosis in Breast Cancer quantitative evaluation system and application |
| CN111082985A (en) * | 2019-12-16 | 2020-04-28 | 厦门大学附属第一医院 | API (application program interface) monitoring method based on open platform |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200051679A1 (en) * | 2018-08-08 | 2020-02-13 | Hc1.Com Inc. | Methods and systems for a pharmacological tracking and reporting platform |
-
2020
- 2020-08-26 CN CN202010871944.1A patent/CN112053756B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007279059A (en) * | 2007-06-18 | 2007-10-25 | Sysmex Corp | Quality management system for clinical examination |
| JP2014182472A (en) * | 2013-03-18 | 2014-09-29 | Mizuho Information & Research Institute Inc | Health care support system, health care support method and health care support program |
| CN104424385A (en) * | 2013-08-22 | 2015-03-18 | 上海联影医疗科技有限公司 | Method and device for evaluating medical images |
| CN105893725A (en) * | 2014-11-13 | 2016-08-24 | 北京众智汇医科技有限公司 | Management system for an entire process of hospital infection prevention and control, and method thereof |
| WO2016130879A1 (en) * | 2015-02-13 | 2016-08-18 | Prairie Ventures, Llc | System and method to objectively measure quality assurance in anatomic pathology |
| CN105936904A (en) * | 2015-06-04 | 2016-09-14 | 佛山市第人民医院 | LncRNA molecule ENST00000438553 and application thereof in auxiliary diagnosis of nasopharyngeal carcinoma |
| CN107545361A (en) * | 2017-08-03 | 2018-01-05 | 广西金域医学检验所有限公司 | Compare System and method between room |
| CN108898328A (en) * | 2018-09-29 | 2018-11-27 | 柳州市妇幼保健院 | A kind of system of the hospital convenient for External quality evaluation |
| CN110423816A (en) * | 2018-10-12 | 2019-11-08 | 天津医科大学肿瘤医院 | Prognosis in Breast Cancer quantitative evaluation system and application |
| CN111082985A (en) * | 2019-12-16 | 2020-04-28 | 厦门大学附属第一医院 | API (application program interface) monitoring method based on open platform |
Non-Patent Citations (1)
| Title |
|---|
| 应用西格玛度量和稳健算法评价不同检测系统糖化血红蛋白检测结果的可比性;康凤凤;单志明;宋超;夏晓华;陈倩;郦卫星;;检验医学(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112053756A (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112053756B (en) | Clinical specimen inspection data-based inspection result quality evaluation method and system | |
| CN107133437B (en) | The method and device that monitoring drug uses | |
| CN111738549A (en) | Food safety risk assessment method, device, equipment and storage medium | |
| CN108509309A (en) | A kind of system and method carrying out performance monitoring based on access log | |
| CN108765177A (en) | Core protects method and core insurance system | |
| CN110729054B (en) | Abnormal diagnosis behavior detection method and device, computer equipment and storage medium | |
| CN115185936B (en) | Medical clinical data quality analysis system based on big data | |
| CN115660262B (en) | Engineering intelligent quality inspection method, system and medium based on database application | |
| CN110210719A (en) | A kind of power equipment static data method for evaluating quality and system | |
| CN112598184A (en) | Method and device for predicting relapse risk of drug-dropping person | |
| CN111832727A (en) | Cross-data, information, knowledge modality and dimension essence identification method and component | |
| CN115545652A (en) | Comprehensive personnel evaluation method, device, equipment and storage medium | |
| CN109346146B (en) | Prescription checking and distributing method, electronic equipment and storage medium | |
| CN116864062B (en) | Health physical examination report data analysis management system based on Internet | |
| CN113990477A (en) | Big data intelligent health monitoring system based on cloud platform | |
| CN108334727B (en) | Method and system for evaluating reliability of toxicological data | |
| CN109118378A (en) | Insurance risk assessment method based on enterprise process data | |
| CN111813922B (en) | High-temperature event detection method and system based on microblog text data | |
| CN111474293B (en) | Method and system for determining bacterial wilt solution | |
| CN115034693A (en) | Biological information data security management method, system and storage medium based on Internet of things | |
| CN114757482A (en) | Method, system, device and medium for analyzing electric quantity use trend of user group | |
| CN114203307A (en) | Subject distribution method and system and electronic equipment | |
| CN113888600A (en) | Trajectory determination method and apparatus, electronic device and computer-readable storage medium | |
| CN117557409B (en) | Intelligent building fire risk visual evaluation management system based on Internet of things | |
| CN113205270B (en) | Method and system for automatically generating satisfaction evaluation table and calculating evaluation score |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |